Neurosymbolic Programming for Science
Abstract
Neurosymbolic Programming (NP) techniques have the potential to accelerate scientific discovery. These models combine neural and symbolic components to learn complex patterns and representations from data, using high-level concepts or known constraints. NP techniques can interface with symbolic domain knowledge from scientists, such as prior knowledge and experimental context, to produce interpretable outputs. We identify opportunities and challenges between current NP models and scientific workflows, with real-world examples from behavior analysis in science: to enable the use of NP broadly for workflows across the natural and social sciences.
Cite
Text
Sun et al. "Neurosymbolic Programming for Science." NeurIPS 2022 Workshops: AI4Science, 2022.Markdown
[Sun et al. "Neurosymbolic Programming for Science." NeurIPS 2022 Workshops: AI4Science, 2022.](https://mlanthology.org/neuripsw/2022/sun2022neuripsw-neurosymbolic/)BibTeX
@inproceedings{sun2022neuripsw-neurosymbolic,
title = {{Neurosymbolic Programming for Science}},
author = {Sun, Jennifer J. and Tjandrasuwita, Megan and Sehgal, Atharva and Solar-Lezama, Armando and Chaudhuri, Swarat and Yue, Yisong and Reyes, Omar Costilla},
booktitle = {NeurIPS 2022 Workshops: AI4Science},
year = {2022},
url = {https://mlanthology.org/neuripsw/2022/sun2022neuripsw-neurosymbolic/}
}